CN108882873B

CN108882873B - Biological information analysis device, system, and program

Info

Publication number: CN108882873B
Application number: CN201780022568.5A
Authority: CN
Inventors: 中嶋宏; 和田洋贵; 土屋直树; 笠井诚朗; 闲绘里子; 上野山彻; 尾林庆一; 小久保绫子; 太田雄也; 志贺利一; 桑原光巨; 佐藤博则; 宫川健; 堤正和
Original assignee: Omron Corp; Omron Healthcare Co Ltd
Current assignee: Omron Corp; Omron Healthcare Co Ltd
Priority date: 2016-04-15
Filing date: 2017-04-14
Publication date: 2021-08-06
Anticipated expiration: 2037-04-14
Also published as: EP3430989B1; JPWO2017179701A1; JPWO2017179695A1; CN109069013A; EP3427656A4; EP3427652A4; EP3427649B1; CN108882872B; EP3427649A1; JP6687263B2; CN108882868A; WO2017179698A1; EP3427648A1; JPWO2017179693A1; EP3430989A4; EP3427651A1; EP3427648A4; US20190117095A1; WO2017179694A1; EP3427655B1

Abstract

The biological information analysis device includes: an index extraction unit that acquires data of a blood pressure waveform in a pre-exercise section before exercise by a user and data of a blood pressure waveform in a post-exercise section after the exercise by the user from time-series data of blood pressure waveforms continuously measured by a sensor that is worn on a body of the user and can non-invasively measure a blood pressure waveform per heartbeat, and extracts an index indicating an influence of the exercise on a cardiac function of the user based on a feature of the blood pressure waveform in the pre-exercise section and a feature of the blood pressure waveform in the post-exercise section; and a processing unit that performs processing based on the extracted index.

Description

Biological information analysis device, system, and program

Technical Field

The present invention relates to a technique for acquiring useful information from a measured blood pressure waveform.

Background

A technique for measuring a change in internal pressure of a radial artery and recording the shape of a pressure pulse wave (blood pressure waveform) is known. Patent document 1 (japanese patent application laid-open No. 2008-61824) discloses measuring a blood pressure waveform by a tension method, and acquiring information such as an AI (Augmentation Index) value, a pulse wave period, a baseline fluctuation rate, a sharpness, and ET (Ejection Time) from the blood pressure waveform. Further, patent document 2 (japanese patent application laid-open No. 2005-532111) discloses that a blood pressure waveform is measured by a wristwatch type sphygmomanometer, and a mean arterial pressure, a mean systolic blood pressure, a mean diastolic blood pressure, a mean systolic blood pressure index, and a mean diastolic blood pressure index are calculated from the blood pressure waveform, and an alarm is output when these values deviate from a reference value.

Documents of the prior art

Patent document

Patent document 1: japanese laid-open patent publication No. 2008-61824

Patent document 2: japanese Kokai publication Hei-2005-532111

Disclosure of Invention

Problems to be solved by the invention

Exercise is known to be effective in improving blood pressure. However, if the movement does not continue for a certain period of time, the decompression effect cannot be obtained. Therefore, the user himself cannot imagine how much the exercise has an effect on himself, and there is a problem that motivation for doing exercise is not increased. In addition, excessive movement may instead become a physical burden. However, since it is not known how much the exercise being performed is loaded on the heart of the user or the like, there is a problem that it is impossible to determine what type of exercise is at risk to the user or what degree of exercise is appropriate.

The present inventors have made an effort to develop a blood pressure measuring device capable of accurately measuring a blood pressure waveform per heartbeat in a free movement. Through experiments by the subject in the development process, the present inventors found that various useful information can be extracted from blood pressure waveform data continuously measured in free motion.

It is therefore an object of the present invention to provide a new technique for evaluating the degree of influence of exercise on cardiac function based on data of blood pressure waveforms.

Technical scheme for solving problems

In order to achieve the above object, the present invention adopts the following configuration.

The biological information analysis device of the present invention is characterized by comprising: an index extraction unit that acquires data of a blood pressure waveform between pre-exercise periods before exercise by a user and data of a blood pressure waveform in a post-exercise period after the user performs the exercise from time-series data of blood pressure waveforms continuously measured by a sensor that is worn on a body of the user and can non-invasively measure a blood pressure waveform for each heartbeat, and extracts an index indicating an effect of the exercise on a cardiac function of the user based on a feature of the blood pressure waveform in the pre-exercise period and a feature of the blood pressure waveform in the post-exercise period; and a processing unit that performs processing based on the extracted index.

The blood pressure waveform is temporarily changed while the exercise is performed, and after the exercise is finished, the blood pressure waveform is gradually restored to the original state. The amount of change in the blood pressure waveform, the recovery time, the recovery speed, and the like at this time depend on the content of the exercise performed or the characteristics related to the cardiac function of the user. Therefore, by using the characteristics of the blood pressure waveform in the pre-exercise section and the characteristics of the blood pressure waveform in the post-exercise section as in the configuration of the present invention, the influence of exercise on the heart function of the user can be quantitatively evaluated.

Specifically, the index extraction unit may calculate the index based on a difference (amount of change) between the characteristics of the blood pressure waveform in the pre-exercise section and the characteristics of the blood pressure waveform in the post-exercise section, may calculate the index based on a time period until the characteristics of the blood pressure waveform changed by the exercise return to the state in the pre-exercise section, or may calculate the index based on a speed at which the characteristics of the blood pressure waveform changed by the exercise return to the state in the pre-exercise section. Further, these may be appropriately combined. As described above, the amount of change in the blood pressure waveform, the recovery time, the recovery speed, and the like depend on the content of the exercise performed or the characteristics relating to the cardiac function of the user. Therefore, by paying attention to at least the amount of change in the blood pressure waveform, the recovery time, and the recovery speed, the influence of the motion on the cardiac function of the user can be appropriately evaluated.

The Index extraction unit may use at least one of characteristics of AI (Augmentation Index), systolic blood pressure, diastolic blood pressure, mean blood pressure, heart rate, time difference between systolic blood pressure and post-systolic blood pressure as the characteristic of the blood pressure waveform. This is because these features have the property of temporarily changing (decreasing or increasing) due to the motion and gradually returning to the original state after the motion.

The processing portion may evaluate a degree of influence of the exercise on cardiac function based on the index and an index related to the intensity and/or duration of the exercise. Further, the processing section may perform processing for outputting the information of the degree of influence. By providing such information, the user can objectively and convincing grasp the exercise performed by himself or the influence of the exercise on his or her heart function.

The present invention may be considered as a biological information analysis device or system having at least a part of the above-described configuration or function. The present invention can be regarded as a biological information analysis method including at least a part of the above-described processing, a program for causing a computer to execute such a method, or a nonvolatile computer-readable recording medium on which such a program is recorded. Each of the above-described constitution and processes may be combined with each other to constitute the present invention as long as a technical contradiction is not created.

Effects of the invention

According to the present invention, a new technique for evaluating the degree of influence of motion on cardiac function based on data of blood pressure waveform can be provided.

Drawings

Fig. 1 is a diagram showing a schematic configuration of an external appearance of a biological information analysis system 10.

Fig. 2 is a block diagram showing a hardware configuration of the biological information analysis system 10.

Fig. 3 is a cross-sectional view schematically showing the structure of the blood pressure measuring unit 20 and the state during measurement.

Fig. 4 is a diagram showing a blood pressure waveform measured by the blood pressure measurement unit 20.

Fig. 5 is a block diagram for explaining the processing of the biological information analysis device 1.

Fig. 6 is a diagram showing a waveform (blood pressure waveform) of a pressure pulse wave of a radial artery with one heartbeat.

Fig. 7 is a flowchart of the process of evaluating the effect of exercise on cardiac function in example 1.

Fig. 8 is a diagram showing the blood pressure waveform and the change in the feature quantity F in the pre-exercise section, the target section (exercise section), and the post-exercise section in example 1.

Fig. 9 is a diagram showing an example of a determination chart of the evaluation result in example 1.

Fig. 10 is a diagram showing an example of evaluation result data in example 1.

Fig. 11 is a diagram showing an example of the index T, V when the AI value is used as the characteristic amount F of the blood pressure waveform in example 1.

Fig. 12 is a diagram showing an example of an information output screen.

Fig. 13 is a diagram showing an example of an information output screen.

Detailed Description

Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings. The following description of each configuration is to be appropriately changed depending on the configuration of an apparatus to which the present invention is applied and various conditions, and is not intended to limit the scope of the present invention to the following description.

< biological information analysis System >

Fig. 1 is a diagram showing a schematic configuration of an external appearance of a biological information analysis system 10 according to an embodiment of the present invention. Fig. 1 shows a state in which the biological information analysis system 10 is worn on the left wrist. The biological information analysis system 10 includes a main body 11 and a belt 12 fixed to the main body 11. The biological information analysis system 10 is a so-called wearable device, and is worn such that the main body portion 11 is in contact with the skin on the inner side of the wrist and the main body portion 11 is disposed on the radial artery TD existing under the skin. In the present embodiment, the device is configured to be worn on the radial artery TD, but may be configured to be worn on another superficial artery.

Fig. 2 is a block diagram showing a hardware configuration of the biological information analysis system 10. The biological information analysis system 10 generally includes a measurement unit 2 and a biological information analysis device 1. The measurement unit 2 is a device that obtains information for analyzing biological information by measurement, and includes a blood pressure measurement unit 20, a body movement measurement unit 21, and an environment measurement unit 22. However, the configuration of the measurement unit 2 is not limited to the configuration shown in fig. 2. For example, a unit for measuring biological information (body temperature, blood glucose, brain waves, and the like) other than blood pressure and body movement may be added. Alternatively, since a unit not used in the embodiment described later is not necessarily configured, it may not be mounted on the biological information analysis system 10. The biological information analysis device 1 is a device that analyzes biological information based on information obtained from the measurement unit 2, and includes a control unit 23, an input unit 24, an output unit 25, a communication unit 26, and a storage unit 27. The units 20-27 are interconnected to exchange signals via a local bus or other signal line. Further, the biological information analysis system 10 has a power source (battery) not shown.

The blood pressure measuring unit 20 is a unit that measures the pressure pulse wave of the radial artery TD by a tensiometry. The tensiometry is a method of non-invasively measuring a pressure pulse wave by a pressure sensor by pressing an artery from above the skin with an appropriate pressure to form a flattened portion on the artery TD to achieve an equilibrium between the intra-arterial pressure and the external pressure.

The body movement measurement unit 21 includes a three-axis acceleration sensor, and is a unit that measures the movement of the user's body (body movement) by the sensor. The body movement measurement unit 21 may comprise circuitry for converting the output of the three-axis acceleration sensor into a format readable by the control unit 23.

The environment measurement unit 22 is a unit for measuring environment information that affects the physical and mental state (particularly, blood pressure) of the user. The environment measuring unit 22 may include, for example, an air temperature sensor, a humidity sensor, an illuminance sensor, an altitude sensor, a position sensor, and the like. The environment measurement unit 22 may include circuitry for converting the outputs of these sensors and the like into a format readable by the control unit 23.

The control unit 23 is a unit responsible for various processes, such as controlling each part of the biological information analysis system 10, acquiring data from the measurement unit 2, storing the acquired data in the storage unit 27, processing/analysis of data, input and output of data, and the like. The control unit 23 includes a hardware processor (hereinafter, referred to as a CPU), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. The processing of the control unit 23 described later is realized by the CPU reading a program stored in the ROM or the storage unit 27. The RAM is used as a work memory when the control unit 23 performs various processes. In the present embodiment, the control unit 23 is configured to execute the acquisition of data from the measurement unit 2 and the storage of data in the storage unit 27, but may be configured to directly store (write) data in the storage unit 27 from the measurement unit 2.

The components of the embodiment, for example, the measurement unit, the index extraction unit, the processing unit, the determination unit, the risk database, the input unit, the output unit, and the case database, may be installed in the biological information analysis system 10 in the form of hardware. The index extraction section, the processing section, and the determination section may receive an executable program stored in the storage unit 27 to execute. The index extraction section, the processing section, and the determination section may receive data from the blood pressure measurement unit 20, the body movement measurement unit 21, the environment measurement unit 22, the input unit 24, the output unit 25, the communication unit 26, the storage unit 27, and the like as necessary. Databases such as risk databases and case databases may be installed in the storage unit 27 or the like, storing information organized so that data can be easily retrieved and accumulated. Here, for example, japanese patent application No. 2016-. The content of which is incorporated by reference in the present specification. Further, japanese patent laid-open No. 2016-. The content of which is incorporated by reference in the present specification.

The input unit 24 is a unit for providing an operation interface to the user. For example, an operation button, a switch, a touch panel, or the like may be used.

The output unit 25 is a unit for providing an interface for outputting information to a user. For example, a display device (a liquid crystal display or the like) which outputs information by an image, a sound output device or a buzzer which outputs information by sound, an LED which outputs information by blinking of light, a vibration device which outputs information by vibration, or the like can be used.

The communication unit 26 is a unit that performs data communication with other devices. As a data communication method, any method such as wireless LAN, Bluetooth (registered trademark) or the like can be used.

The storage unit 27 is a storage medium capable of storing and reading data for storing a program executed in the control unit 23, measurement data obtained from each measurement unit, various data obtained by processing the measurement data, and the like. The storage unit 27 is a medium that accumulates information to be stored by an electric, magnetic, optical, mechanical, or chemical action. For example, a flash memory is used. The storage unit 27 may be a portable unit such as a memory card, or may be built in the biological information analysis system 10.

Some or all of the body movement measurement unit 21, the environment measurement unit 22, the control unit 23, the input unit 24, the output unit 25, and the storage unit 27 may be constituted by a device different from the main body portion 11. That is, as long as the main body 11 incorporating the blood pressure measuring unit 20 and the circuits for controlling the blood pressure measuring unit can be worn on the wrist, the configuration of the other units can be freely designed. In this case, the main body portion 11 cooperates with another unit through the communication unit 26. For example, various configurations are conceivable in which the functions of the control unit 23, the input unit 24, and the output unit 25 are configured by an application of a smartphone, or necessary data is acquired from an activity meter having the functions of the body movement measurement unit 21 and the environment measurement unit 22. Further, a sensor for measuring biological information other than blood pressure may be provided. For example, a sleep sensor, pulse oximeter (SpO) may be combined₂Sensors), respiration sensors (flow sensors), blood glucose sensors, and the like.

In the present embodiment, the sensor for measuring blood pressure (blood pressure measuring means 20) and the structure (control means 23 and the like) for analyzing and processing blood pressure waveform data are provided in one device, but they may be separately provided. In the present embodiment, a configuration (such as the control unit 23) for performing analysis processing of biological information is referred to as a biological information analysis device, and a device configured by a combination of a measurement unit and a biological information analysis device is referred to as a biological information analysis system. However, the names are for convenience, and all of the configurations of the measurement unit and the analysis processing of the biological information may be referred to as biological information analysis devices, or other names may be used.

< measurement of blood pressure waveform >

Fig. 3 is a cross-sectional view schematically showing the structure of the blood pressure measuring unit 20 and the state during measurement. The blood pressure measuring unit 20 includes a pressure sensor 30 and a pressing mechanism 31 for pressing the pressure sensor 30 against the wrist. The pressure sensor 30 has a plurality of pressure detecting elements 300. The pressure detection element 300 is an element for detecting pressure and converting it into an electric signal, and for example, an element utilizing piezoresistive effect or the like can be preferably used. The pressing mechanism 31 is constituted by, for example, a bag and a pump for adjusting the internal pressure of the bag. When the control unit 23 controls the pump to increase the internal pressure of the air bag, the pressure sensor 30 is pressed against the skin surface due to the inflation of the air bag. The pressing mechanism 31 may be any mechanism that can adjust the pressing force of the pressure sensor 30 against the skin surface, and is not limited to the use of an air bag.

When the biological information analysis system 10 is worn on the wrist and started, the control unit 23 controls the pressing mechanism 31 of the blood pressure measurement unit 20 to maintain the pressing force of the pressure sensor 30 in an appropriate state (tension state). Then, the pressure signal detected by the pressure sensor 30 is sequentially acquired by the control unit 23. The analog physical quantity (for example, a voltage value) output from the pressure detection element 300 is digitized by an a/D conversion circuit or the like of a well-known technique, thereby generating a pressure signal obtained by the pressure sensor 30. The analog physical quantity may be an appropriate analog value such as a current value or a resistance value depending on the type of the pressure detection element 300. The signal processing such as a/D conversion may be performed by providing a predetermined circuit in the blood pressure measurement unit 20, or may be performed by another unit (not shown) provided between the blood pressure measurement unit 20 and the control unit 23. The pressure signal acquired by the control unit 23 corresponds to an instantaneous value of the internal pressure of the radial artery TD. Therefore, by acquiring the pressure signal continuously with time granularity which can grasp the blood pressure waveform of one heartbeat, time-series data of the blood pressure waveform can be acquired. The control unit 23 stores the pressure signals sequentially acquired by the pressure sensor 30 in the storage unit 27 together with information on the measurement time thereof. The control unit 23 may store the acquired pressure signal directly in the storage unit 27, or may store the pressure signal in the storage unit 27 after performing necessary signal processing on the pressure signal. The required information processing may include, for example, processing of calibrating the pressure signal so that the amplitude of the pressure signal coincides with the blood pressure value (e.g., upper arm blood pressure), processing of reducing or removing noise of the pressure signal, and the like.

Fig. 4 shows a blood pressure waveform measured by the blood pressure measuring unit 20. The horizontal axis is time and the vertical axis is blood pressure. The sampling frequency can be arbitrarily set, but is preferably set to 100Hz or more in order to reproduce the shape characteristics of the waveform of one heartbeat. Since the period of one heart beat is about 1 second, about 100 or more data points are acquired for the waveform of one heart beat.

The blood pressure measuring unit 20 of the present embodiment has the following advantages.

The blood pressure waveform of each heartbeat can be measured. Thus, for example, various indicators relating to blood pressure, heart state, cardiovascular risk, and the like can be obtained based on the shape characteristics of the blood pressure waveform. Further, since the instantaneous value of the blood pressure can be monitored, it is possible to detect the blood pressure fluctuation (rapid rise of the blood pressure value) immediately or to detect the blood pressure fluctuation and the disturbance of the blood pressure waveform occurring in a very short time (one to several heartbeats) without omission.

As a portable sphygmomanometer, a sphygmomanometer of a type that is worn on a wrist or an upper arm and measures blood pressure by an oscillometric method is practically used. However, in the conventional portable sphygmomanometer, the average value of the blood pressure can be measured only from the fluctuation of the cuff internal pressure of a plurality of heart beats between several seconds and ten seconds, and the time series data of the blood pressure waveform per one heart beat cannot be obtained as in the blood pressure measuring unit 20 of the present embodiment.

Time series data of the blood pressure waveform may be recorded. By acquiring time-series data of a blood pressure waveform, for example, various indexes related to blood pressure or heart state, cardiovascular risk, and the like can be obtained by grasping features regarding time variation of the blood pressure waveform, or by performing frequency analysis on the time-series data to extract specific frequency components.

Since the device is of a portable (wearable) type, the burden of measurement on the user is small, and it is relatively easy to perform continuous measurement for a long time, and further to perform blood pressure monitoring for 24 hours. Further, since it is portable, it is possible to measure not only the blood pressure at rest but also the change in blood pressure in free movement (for example, in daily life or exercise). This makes it possible to grasp the influence of, for example, movement (sleep, meal, commute, work, medicine taking, etc.) or exercise on blood pressure in daily life.

The conventional product is a device of a type in which an arm and a wrist are fixed to a blood pressure measurement unit and measurement is performed in a stationary state, and it is impossible to measure a blood pressure change in daily life or exercise as in the biological information analysis system 10 of the present embodiment.

Easily combined or coordinated with other sensors. For example, evaluation and integrated evaluation can be performed with information obtained by other sensors (environmental information such as body movement, temperature, SpO, etc.), and₂and other biological information such as respiration).

< biological information analysis device >

Fig. 5 is a block diagram for explaining the processing of the biological information analysis device 1. As shown in fig. 5, the biological information analysis device 1 includes an index extraction unit 50 and a processing unit 51. In the present embodiment, the processes of the index extraction section 50 and the processing section 51 can be realized by executing a necessary program by the control unit 23. The program may be stored in the storage unit 27. When the control unit 23 executes a necessary program, the program to be an object stored in the ROM or the storage unit is developed in the RAM. Then, the control unit 23 interprets and executes the program developed in the RAM by the CPU, thereby controlling each constituent element. Part or all of the processing of the index extraction unit 50 and the processing unit 51 may be constituted by circuits such as an ASIC and an FPGA. Alternatively, a part or all of the processes in the index extraction section 50 and the processing section 51 may be realized by a computer (for example, a smartphone, a tablet terminal, a personal computer, a cloud service, or the like) separate from the main body section 11.

The index extraction unit 50 acquires time-series data of blood pressure waveforms continuously measured by the blood pressure measurement unit 20 from the storage unit 27. The index extraction unit 50 extracts an index relating to a blood pressure waveform feature from the acquired time-series data of the blood pressure waveform. Here, the characteristics of the blood pressure waveform include shape characteristics of the blood pressure waveform of one heartbeat, temporal changes of the blood pressure waveform, frequency components of the blood pressure waveform, and the like. However, the characteristics of the blood pressure waveform are not limited thereto. The extracted index is output to the processing unit 51. There are various characteristics and indices of the blood pressure waveform, and the characteristics and indices to be extracted may be appropriately designed or selected according to the purpose of processing by the processing section 51. Features and indices extractable from the measurement data of the blood pressure waveform of the present embodiment will be described in detail later.

When acquiring the index, the index extraction section 50 may use the measurement data of the body movement measurement unit 21 and/or the measurement data of the environment measurement unit 22 in addition to the measurement data of the blood pressure waveform. Further, although not shown, a sleep sensor, SpO, may also be combined₂Measurement data of a sensor, a respiration sensor (flow sensor), a blood glucose sensor, and the like. By comprehensively analyzing a variety of measurement data obtained by a variety of sensors, more sophisticated information analysis of the blood pressure waveform can be performed. The blood pressure waveform data may be classified for each state of the user, such as at rest and at motion, at high and low temperature, at light and deep sleep, at breathing and at apnea. Alternatively, it can also be evaluatedThe causal relationship and correlation of each measurement data, such as extracting the influence of body movement, activity amount and activity intensity, temperature change, apnea, breathing pattern, etc. on blood pressure.

The processing unit 51 receives the index extracted by the index extraction unit 50. The processing unit 51 performs processing based on the received index. Various processes can be conceived among the index-based processes. For example, the processing unit 51 may present the value, change, and the like of the extracted index to the user, a doctor, a health care professional, and the like, and encourage use in health management, treatment, health care guidance, and the like. Alternatively, the processing unit 51 may estimate cardiovascular risk from the extracted index, or present a guideline for maintaining health or improving risk. When an increase in cardiovascular risk is detected or predicted based on the index, the processing unit 51 may notify the user, the attending physician, or the like, or perform control for preventing an action that is a burden on the heart or the like of the user or occurrence of a cardiovascular event.

< information obtained from blood pressure waveform >

Fig. 6 shows a waveform (blood pressure waveform) of a pressure pulse wave of a radial artery of one heartbeat. The horizontal axis represents time t [ msec ], and the vertical axis represents blood pressure BP [ mmHg ].

The blood pressure waveform is a composite wave of a "forward wave" generated when the heart contracts and sends blood and a "reflected wave" generated when the forward wave is reflected by a branch portion of a peripheral blood vessel or an artery. An example of characteristic points that can be extracted from a blood pressure waveform of one heartbeat is shown below.

The point F1 is the rising point of the pressure pulse wave. Point F1 corresponds to the starting point of the cardiac ejection, i.e. the opening point of the aortic valve.

Point F2 is the point (first peak) at which the amplitude (pressure) of the forward wave is maximum.

The point F3 is an inflection point that appears on the way of the forward wave descent due to the overlapping of the reflected waves.

Point F4 is the minimum point occurring between the forward wave and the reflected wave, also called the notch. This corresponds to the closing point of the aortic valve.

The point F5 is the peak (second peak) of the reflected wave that appears after the point F4.

Point F6 is the end point of one heartbeat, corresponding to the ejection start point of the next heartbeat, i.e. the start point of the next heartbeat.

The index extraction section 50 may use any algorithm to detect the above-described feature points. For example, the index extraction unit 50 may extract a characteristic point (inflection point) of the blood pressure waveform by calculating an n-order differential waveform of the blood pressure waveform and detecting a zero-crossing point thereof (for points F1, F2, F4, F5, and F6, it may be detected from a first-order differential waveform, and for point F3, it may be detected from a second-order differential waveform or a fourth-order differential waveform). Alternatively, the index extraction unit 50 may read a waveform pattern in which the feature points are arranged in advance from the storage unit 27, and determine the positions of the feature points by fitting the waveform pattern to the blood pressure waveform targeted for the feature points.

Based on the time t and the pressure BP of the feature points F1 to F6, the index extraction unit 50 can calculate various information (values, feature values, indexes, and the like) from the blood pressure waveform of one heartbeat. Hereinafter, representative information among information obtainable from a blood pressure waveform is exemplified. Tx and BPx represent the time and blood pressure of the feature point Fx, respectively.

Pulse wave interval (heart cycle) TA ═ t6-t1

Heart rate PR ═ 1/TA

The pulse wave rise time UT-t 2-t1

Contraction period TS ═ t4-t1

Diastolic time TD ═ t6-t4

The reflected wave delay time t3-t1

Hypertension (systolic blood pressure) SBP ═ BP2

The lowest blood pressure (diastolic blood pressure) DBP ═ BP1

Area of blood pressure waveform/cardiac cycle TA of t1 to t6 average blood pressure MAP

Systolic mean blood pressure (t 1-t 4) area of blood pressure waveform/systolic TS

Mean blood pressure in diastolic phase (area of blood pressure waveform from t4 to t 6)/diastolic phase TD

Pulse pressure PP ═ hypertension SBP-hypotension DBP

Post-shrink compression SBP2 ═ BP3

AI (Augmentation Index) (post-systolic pressure SBP 2-hypotension DBP)/pulse pressure PP

Basic statistics of these pieces of information (values, feature quantities, indices) can also be used as indices. The basic statistical quantities include, for example, representative values (mean value, median value, mode value, maximum value, minimum value, etc.), degrees of dispersion (variance, standard deviation, coefficient of variation, etc.). In addition, temporal changes in these pieces of information (values, characteristic values, indices) can also be used as indices.

Further, the index extraction section 50 may obtain an index of BRS (blood pressure regulation ability) by calculating a plurality of pieces of heartbeat information. This is an index indicating the ability to regulate blood pressure to be constant. The calculation method may be, for example, a Spontaneous sequence (Spontaneous sequence) method or the like. The method comprises the steps of extracting sequences of synchronous rising or falling of the SBP and the TA of the pulse wave of the hypertension which is more than three continuous heartbeats, drawing the SBP and the TA of the pulse wave on a two-dimensional plane, and defining the slope of a regression line obtained by a least square method as BRS.

As described above, by using the biological information analysis system 10 of the present embodiment, various kinds of information can be acquired from the data of the blood pressure waveform. It is not necessary to install a function for acquiring all of the above information in the biological information analysis system 10. Depending on the configuration of the biological information analysis system 10, the user, the purpose of use, the place of use, and the like, only a function for acquiring necessary information may be installed. Each function may be provided as a program module (application software), and the necessary program module may be installed in the biological information analysis system 10 to add the function.

Hereinafter, an example of a specific application of the biological information analysis system 10 will be exemplarily described.

< example 1 >

The present invention relates to a method for assessing and visualizing the effect of motion on the cardiac function of a user based on time series data of blood pressure waveforms.

It is known that exercise is effective for improvement of blood pressure, but it is difficult for the user himself to imagine how much exercise has an effect on himself. In addition, excessive exercise may be a burden on the body, but it is difficult to know how much the exercise is being performed on the heart of the user.

The present inventors observed the change in the blood pressure waveform before and after exercise in an experiment of a subject, and confirmed the following phenomenon. (1) The blood pressure waveform temporarily changes due to the movement, and gradually returns to the original state after the movement is finished. (2) The amount of change in the blood pressure waveform and the recovery time (recovery speed) vary depending on the activity amount of the exercise performed. (3) In addition, the amount of change in the blood pressure waveform and the recovery time (recovery speed) also vary depending on the cardiovascular characteristics of a human. Based on such knowledge, an algorithm for evaluating the degree of influence of motion on cardiac function based on time-series data of a blood pressure waveform is provided in the present embodiment.

The biological information analysis device 1 of the present embodiment estimates the cardiovascular characteristics of the user by estimating the change in the blood pressure waveform before and after exercise using the feature amount extracted from the blood pressure waveform for each heartbeat. As the characteristic amount of the blood pressure waveform, for example, AI (Augmentation Index), systolic blood pressure SBP, diastolic blood pressure DBP, mean blood pressure MAP, heart rate PR, time difference DTp between systolic blood pressure SBP and systolic post-pressure SBP2 (t 3-t2 in fig. 6), and the like can be used. Of these feature values, AI, SBP, DBP, and MAP show actions of temporarily decreasing due to motion and then gradually increasing and returning to the original values. On the other hand, PR and DTp show actions of gradually decreasing and returning to the original states after temporarily increasing due to the movement. By accurately monitoring the blood pressure waveform per heartbeat in the free movement, such a change in the characteristic amount can be grasped at the first time. In other words, from blood pressure data obtained from a sphygmomanometer of a type that cannot measure a blood pressure waveform like the oscillometric method and blood pressure data measured at rest, the change in the feature amount as described above cannot be grasped.

The flow of the processing of the present embodiment is explained with reference to the flowchart of fig. 7. However, as a premise, it is assumed that time-series data of the blood pressure waveform measured by the blood pressure measuring unit 20 and time-series data of the activity intensity [ METs ] measured by the body movement measuring unit 21 are accumulated in the storage unit 27 or other data storage (including a cloud server).

The index extraction unit 50 reads time-series data of the blood pressure waveform and time-series data of the activity intensity from the storage unit 27 or other data memory (step 4800). Then, the index extraction unit 50 detects a section in which exercise is performed from the time-series data of the activity intensity, and marks a section in which exercise is to be set as an evaluation target (step 4801). For example, a section satisfying the following conditions (1) to (3) is designated as an evaluation target.

(1) Exercise (e.g., a state in which the activity intensity is greater than 1 METs) continues for a time Tth1 or more. The activity intensity may be not only the METs but also other indicators (for example, METs × body weight, etc.) indicating the activity intensity.

(2) The activity (Ex) in the entire interval is equal to or higher than the threshold value Eth. Here, the Ex amount (exercise amount) is a value of the activity intensity [ METs ] multiplied by the activity time.

(3) Before and after this interval, a non-moving interval (for example, an interval in which a state in which the activity intensity is 1METs or less continues) having a length of not less than Tth2 exists.

The condition (2) may be determined not by the amount of activity but by other indicators related to the exercise (for example, statistics of the exercise intensity (average, maximum, most frequent value, etc.) or calorie consumption in the section). The threshold value Tth1 is set to a time longer than at least the minimum time unit in which the activity intensity can be measured. In addition, the threshold Tth2 is set to a length to such an extent that the motion performed in the other section does not affect the evaluation. The threshold values Tth1, Tth2, Eth used for the above determination processing are stored in advance in, for example, the storage unit 27.

Hereinafter, the motion section marked as the evaluation target is referred to as a "target section", the activity amount of the target section is Ea, and the activity time (length) is Ta.

Then, the index extraction unit 50 acquires blood pressure waveform data of a non-moving section immediately before the target section (referred to as a "pre-moving section") and blood pressure waveform data of a non-moving section immediately after the target section (referred to as a "post-moving section") from the time-series data of the blood pressure waveform (step 4802). The lengths of the pre-exercise interval and the post-exercise interval may be set arbitrarily (the longest length is not more than Tth 2). Fig. 8 shows the blood pressure waveform and the change in the characteristic amount F thereof in the pre-exercise section, the target section (exercise section), and the post-exercise section. Fig. 8 shows an example of using an AI value as the characteristic amount F of the blood pressure waveform.

The index extraction unit 50 calculates a value F1 of the feature quantity F before the exercise based on the blood pressure waveform data of the pre-exercise section (step 4803). For example, the value F1 is an average value, a most frequent value, a median value, or the like of the feature quantity F of the pre-movement section. Further, the index extraction unit 50 calculates a minimum value F2 of the feature quantity F in the post-exercise section based on the blood pressure waveform data of the post-exercise section (step 4804). Then, the index extraction section 50 calculates the change amount DF (═ F1-F2|) (step 4805). The index DF indicates how much the blood pressure waveform has changed due to the movement performed in the target section, and can be used as an index indicating the influence of the movement on the cardiac function of the user. If the feature quantity (for example, PR, DTp, or the like) increases due to the motion, the maximum value F2 of the feature quantity F in the post-motion section may be calculated in step 4804.

Then, the processing unit 51 evaluates the degree of influence of the exercise on the cardiac function based on the activity amount Ea and the index DF in the target section (step 4806). In the present embodiment, any one of Low (degree of influence: small), Mid (degree of influence: medium), and High (degree of influence: large) values is output as the evaluation result R. For example, when the index DF is small (the amount of change in the feature quantity F is small) although the activity amount Ea is large, it is estimated that the cardiac function is hardly affected by the motion. Fig. 9 is an example of a determination chart of the evaluation result R used by the processing unit 51. The horizontal axis represents the value of index DF, and the vertical axis represents the activity [ Ex ]. The reference or determination chart for evaluation is merely an example, and other methods may be used. Also, for example, there may be soft classification, continuous value evaluation instead of three-phase evaluation.

For example, the processing unit 51 generates evaluation result data in which information of the target section (the date and time of the start of exercise, the amount of activity Ea, and the activity time Ta), information of the pre-exercise section and the post-exercise section (the feature amounts F1 and F2), the degree of influence index (DF), and the evaluation result (R) are associated with each other, and stores the evaluation result data in the storage unit 27 (step 4807). If a plurality of target sections are extracted in step 4800, the processing of steps 4802 to 4807 is repeated for each target section.

Fig. 10 is an example of evaluation result data accumulated in the storage unit 27. By accumulating such evaluation result data, it is possible to objectively grasp how much the degree of exercise has largely affected the heart function of the user. In this way, for example, the user knows how much exercise the user's own heart function is currently being affected by, so that the effect of motor therapy can be imagined and the motivation for continuous exercise can be increased. Furthermore, the user may be aware of the exercise regimen for his or her own risks and may select a reasonable exercise regimen that suits his or her cardiovascular characteristics. Furthermore, a doctor or healthcare professional can guide the appropriate exercise therapy matching the cardiovascular characteristics of the user by utilizing such data.

(modification example)

As an index indicating the influence of exercise on the cardiac function, the following index may be used in addition to the change amount DF of the feature quantity F.

Time T required for the feature quantity F changed by the movement to return to the state of the pre-movement section

The speed V at which the characteristic amount F changed by the movement returns to the state of the pre-movement section

The index T, V is an index indicating the restoring force of the cardiac function of the user. Fig. 11 shows an example of the index T, V when an AI value is used as the feature quantity F of the blood pressure waveform. The index T, V is calculated as follows.

T＝T3-T2

V＝|F3-F2|/|T3-T2|

Here, T2 is the time at which the feature value F is minimum in the post-exercise section, and F2 is the value of the feature value at time T2. T3 is the time at which the feature value F returns to the value F1 before the motion in the section after the motion, and F3 is the value of the feature value at the time T3. It should be noted that a certain margin may be set to determine whether or not to recover. For example, it is determined as "recovered" or the like, considering that the difference or ratio between the value F1 of the feature amount before the movement and the value F3 of the feature amount after the movement falls within a predetermined range. Note that, when Te is the end time of the target section (the time at which the movement is ended), the index T, V can be obtained as follows.

T＝T3-Te

V＝|F3-Fe|/|T3-Te|

Fe is the value of the characteristic amount at time Te.

Like index DF, the use of index T, V also allows assessment of the degree of influence motion has on cardiac function. In the evaluation of the degree of influence, a plurality of indexes may be used. For example, in fig. 9, a two-axis graph of the activity amount Ea and the index DF is used, but the influence degree may be determined by a three-axis graph of the activity amount Ea, the index DF, and the index T, or by a four-axis graph of the activity amount Ea, the index DF, the index T, and the index V. The combination and number of the indices used for the determination are arbitrary.

(output example)

The processing section 51 may output information obtained by the above-described evaluation processing to the output unit 25 or an external display device or the like (not shown). Fig. 12 is an example of an information output screen of the processing unit 51. The upper part shows a graph of information on the exercise section (activity Ea), the blood pressure waveform and the characteristic value (AI value) in the pre-exercise section and the post-exercise section, and the lower part shows a graph showing the evaluation result (asterisk) of the exercise section. By observing such a screen, the user can easily grasp the degree of influence of the exercise performed on the heart function of the user. Fig. 13 is another example of the information output screen of the processing unit 51. The evaluation results (asterisks) related to the motions of a plurality of times (4 times in the example of fig. 13) are arranged in time series and displayed on a graph. By observing such a screen, the user can intuitively grasp the change in the degree of influence of the exercise performed on the heart function of the user.

The configuration of the above embodiments and examples is only one specific example of the present invention, and is not intended to limit the scope of the present invention. The present invention may be embodied in various specific configurations without departing from the technical spirit thereof.

The technical idea disclosed in the present specification can also be determined as the following invention.

(appendix 1)

A biological information analysis device is characterized in that,

having a hardware processor and a memory for storing programs,

the hardware processor is connected with the computer through the program,

acquiring data of a blood pressure waveform in a pre-exercise section before exercise by a user and data of a blood pressure waveform in a post-exercise section after the exercise by the user from time-series data of blood pressure waveforms continuously measured by a sensor that is worn on a body of the user and is capable of non-invasively measuring a blood pressure waveform for each heartbeat,

extracting an index representing an influence of the motion on a cardiac function of the user based on the feature of the blood pressure waveform in the pre-motion section and the feature of the blood pressure waveform in the post-motion section,

and performing processing based on the extracted index.

(appendix 2)

A biological information analysis system characterized in that,

comprising: a sensor worn on the body of the user and capable of non-invasively measuring the blood pressure waveform for each heartbeat; a hardware processor; and a memory for storing a program, and,

the hardware processor is connected with the computer through the program,

and performing processing based on the extracted index.

(appendix 3)

A biological information analysis method, comprising:

a step of acquiring, by at least one hardware processor, data of a blood pressure waveform in a pre-exercise section before exercise by a user and data of a blood pressure waveform in a post-exercise section after the user performs the exercise, from time-series data of blood pressure waveforms continuously measured by a sensor that is worn on the body of the user and is capable of non-invasively measuring a blood pressure waveform for each heartbeat;

a step of extracting, by at least one hardware processor, an index representing an influence of the exercise on a cardiac function of the user based on the feature of the blood pressure waveform in the pre-exercise section and the feature of the blood pressure waveform in the post-exercise section; and

and a step of performing, by at least one hardware processor, processing based on the extracted index.

Description of the reference numerals

1: biological information analysis device, 2: measuring unit

10: biological information analysis system, 11: main body portion, 12: belt

20: blood pressure measurement unit, 21: body movement measurement unit, 22: environment measurement unit, 23: control unit, 24: input unit, 25: output unit, 26: communication unit, 27: memory cell

30: pressure sensor, 31: pressing mechanism, 300: pressure detecting element

50: index extraction unit, 51: treatment section

Claims

1. A biological information analysis device is characterized by comprising:

an index extraction unit that acquires data of a blood pressure waveform in a pre-exercise section before exercise by a user and data of a blood pressure waveform in a post-exercise section after the exercise by the user from time-series data of blood pressure waveforms continuously measured by a sensor that is worn on a body of the user and can non-invasively measure a blood pressure waveform per heartbeat, and extracts an index indicating an influence of the exercise on a cardiac function of the user based on a feature of the blood pressure waveform in the pre-exercise section and a feature of the blood pressure waveform in the post-exercise section; and

a processing unit that performs processing based on the extracted index,

the index extraction unit detects a section satisfying the following conditions (1) to (3) as a movement section in which the user has moved:

(1) the state that the activity intensity is larger than the first value is continuous for more than a first time;

(2) the activity level of the whole interval is above a specified threshold;

(3) there are intervals in which the state in which the activity intensity is equal to or lower than the first value continues for a second time or longer before and after,

the index extraction unit extracts the index based on an average value, a median value, or a mode of the characteristics of the blood pressure waveform in the pre-exercise section and a maximum value or a minimum value of the characteristics of the blood pressure waveform in the post-exercise section.

2. The biological information analysis device according to claim 1,

the index extraction unit calculates the index based on a difference between an average value, a median value, or a mode of the characteristics of the blood pressure waveform in the pre-exercise section and a maximum value or a minimum value of the characteristics of the blood pressure waveform in the post-exercise section.

3. The biological information analysis device according to claim 1,

the index extraction unit calculates the index based on a time until the feature of the blood pressure waveform in the post-exercise section is restored from a maximum value or a minimum value of the feature of the blood pressure waveform in the post-exercise section to an average value, a median value, or a mode value of the feature of the blood pressure waveform in the pre-exercise section.

4. The biological information analysis device according to claim 1,

the index extraction unit calculates the index based on a speed at which the feature of the blood pressure waveform in the post-exercise section is restored from the maximum value or the minimum value of the feature of the blood pressure waveform in the post-exercise section to the average value, the intermediate value, or the mode of the feature of the blood pressure waveform in the pre-exercise section.

5. The biological information analysis device according to claim 1,

the index extraction unit uses at least one of the characteristics of an emphasis index, systolic blood pressure, diastolic blood pressure, average blood pressure, heart rate, and time difference between systolic blood pressure and post-systolic blood pressure as the characteristic of the blood pressure waveform.

6. The biological information analysis device according to claim 1,

the processing portion evaluates a degree of influence of the exercise on cardiac function based on the index and an index relating to intensity and/or duration of the exercise.

7. The biological information analysis device according to claim 6,

the processing unit performs processing for outputting the information of the degree of influence.

8. A biological information analysis system comprising:

a sensor worn on the body of the user and capable of non-invasively measuring the blood pressure waveform for each heartbeat; and

the biological information analysis device according to any one of claims 1 to 7, wherein the biological information is analyzed using data of a blood pressure waveform continuously measured by the sensor.

9. A computer-readable storage medium storing a program characterized in that,

the program causes a processor to function as the index extraction unit and the processing unit of the biological information analysis device according to any one of claims 1 to 7.

10. A biological information analysis method, comprising:

an index extraction step of acquiring data of a blood pressure waveform in a pre-exercise section before exercise by a user and data of a blood pressure waveform in a post-exercise section after the exercise by the user from time-series data of blood pressure waveforms continuously measured by a sensor that is worn on a body of the user and capable of non-invasively measuring a blood pressure waveform per heartbeat, and extracting an index representing an influence of the exercise on a cardiac function of the user based on a feature of the blood pressure waveform in the pre-exercise section and a feature of the blood pressure waveform in the post-exercise section; and

a processing step of performing processing based on the extracted index,

in the index extraction step, a section satisfying the following conditions (1) to (3) is detected as a movement section in which the user has moved:

(2) the activity level of the whole interval is above a specified threshold;

in the extracting the index, the index is extracted based on an average value, a median value, or a mode of the features of the blood pressure waveform in the pre-exercise section and a maximum value or a minimum value of the features of the blood pressure waveform in the post-exercise section.